
"""simple docstring"""


import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
      from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor



class 	__a	:






       def __init__(	self			:				int							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				Optional[Any]=13							,   UpperCAmelCase_			:				List[str]=32							,   UpperCAmelCase_			:				Optional[Any]=3							,   UpperCAmelCase_			:				List[str]=4							,   UpperCAmelCase_			:				Dict=[10, 20, 30, 40]							,   UpperCAmelCase_			:				Dict=[2, 2, 3, 2]							,   UpperCAmelCase_			:				str=True							,   UpperCAmelCase_			:				Any=True							,   UpperCAmelCase_			:				Any=37							,   UpperCAmelCase_			:				Dict="gelu"							,   UpperCAmelCase_			:				Optional[Any]=10							,   UpperCAmelCase_			:				int=0.02							,   UpperCAmelCase_			:				Any=["stage2", "stage3", "stage4"]							,   UpperCAmelCase_			:				Tuple=[2, 3, 4]							,   UpperCAmelCase_			:				str=None							,   )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		image_size
              UpperCamelCase       			=		num_channels
              UpperCamelCase       			=		num_stages
              UpperCamelCase       			=		hidden_sizes
              UpperCamelCase       			=		depths
              UpperCamelCase       			=		is_training
              UpperCamelCase       			=		use_labels
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		num_labels
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		out_features
              UpperCamelCase       			=		out_indices
              UpperCamelCase       			=		scope






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

              UpperCamelCase       			=		None
              if self.use_labels:
                     UpperCamelCase       			=		ids_tensor([self.batch_size]							,   self.num_labels      )

              UpperCamelCase       			=		self.get_config()

              return config, pixel_values, labels






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Dict:







              """simple docstring"""
              return ConvNextVaConfig(
                  num_channels=self.num_channels							,   hidden_sizes=self.hidden_sizes							,   depths=self.depths							,   num_stages=self.num_stages							,   hidden_act=self.hidden_act							,   is_decoder=lowerCAmelCase__							,   initializer_range=self.initializer_range							,   out_features=self.out_features							,   out_indices=self.out_indices							,   num_labels=self.num_labels							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Union[str, Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		ConvNextVaModel(config=lowerCAmelCase__      )
              model.to(lowerCAmelCase__      )
              model.eval()
              UpperCamelCase       			=		model(lowerCAmelCase__      )
              # expected last hidden states: B, C, H // 32, W // 32
              self.parent.assertEqual(
                  result.last_hidden_state.shape							,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				List[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		ConvNextVaForImageClassification(lowerCAmelCase__      )
              model.to(lowerCAmelCase__      )
              model.eval()
              UpperCamelCase       			=		model(lowerCAmelCase__							,   labels=lowerCAmelCase__      )
              self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		ConvNextVaBackbone(config=lowerCAmelCase__      )
              model.to(lowerCAmelCase__      )
              model.eval()
              UpperCamelCase       			=		model(lowerCAmelCase__      )

              # verify hidden states
              self.parent.assertEqual(len(result.feature_maps      )							,   len(config.out_features      )      )
              self.parent.assertListEqual(list(result.feature_maps[0].shape      )							,   [self.batch_size, self.hidden_sizes[1], 4, 4]      )

              # verify channels
              self.parent.assertEqual(len(model.channels      )							,   len(config.out_features      )      )
              self.parent.assertListEqual(model.channels							,   config.hidden_sizes[1:]      )

              # verify backbone works with out_features=None
              UpperCamelCase       			=		None
              UpperCamelCase       			=		ConvNextVaBackbone(config=lowerCAmelCase__      )
              model.to(lowerCAmelCase__      )
              model.eval()
              UpperCamelCase       			=		model(lowerCAmelCase__      )

              # verify feature maps
              self.parent.assertEqual(len(result.feature_maps      )							,   1      )
              self.parent.assertListEqual(list(result.feature_maps[0].shape      )							,   [self.batch_size, self.hidden_sizes[-1], 1, 1]      )

              # verify channels
              self.parent.assertEqual(len(model.channels      )							,   1      )
              self.parent.assertListEqual(model.channels							,   [config.hidden_sizes[-1]]      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		self.prepare_config_and_inputs()
              UpperCamelCase       			=		config_and_inputs
              UpperCamelCase       			=		{'''pixel_values''': pixel_values}
              return config, inputs_dict






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		self.prepare_config_and_inputs()
              UpperCamelCase       			=		config_and_inputs
              UpperCamelCase       			=		{'''pixel_values''': pixel_values, '''labels''': labels}
              return config, inputs_dict





@require_torch
class 	__a	(   snake_case_     ,  snake_case_     ,  unittest.TestCase						):
       UpperCamelCase_		:       Optional[Any]				=      (
           (
               ConvNextVaModel,
               ConvNextVaForImageClassification,
               ConvNextVaBackbone,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase_		:       int				=      (
           {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCamelCase_		:       Tuple				=      False
       UpperCamelCase_		:       int				=      False
       UpperCamelCase_		:       Dict				=      False
       UpperCamelCase_		:       List[str]				=      False
       UpperCamelCase_		:       Any				=      False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		ConvNextVaModelTester(self      )
              UpperCamelCase       			=		ConfigTester(self							,   config_class=lowerCAmelCase__							,   has_text_modality=lowerCAmelCase__							,   hidden_size=37      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Any:







              """simple docstring"""
              self.create_and_test_config_common_properties()
              self.config_tester.create_and_test_config_to_json_string()
              self.config_tester.create_and_test_config_to_json_file()
              self.config_tester.create_and_test_config_from_and_save_pretrained()
              self.config_tester.create_and_test_config_with_num_labels()
              self.config_tester.check_config_can_be_init_without_params()
              self.config_tester.check_config_arguments_init()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Tuple:







              """simple docstring"""
              return






       @unittest.skip(reason="ConvNextV2 does not use inputs_embeds"      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Tuple:







              """simple docstring"""
              pass






       @unittest.skip(reason="ConvNextV2 does not support input and output embeddings"      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	List[Any]:







              """simple docstring"""
              pass






       @unittest.skip(reason="ConvNextV2 does not use feedforward chunking"      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Tuple:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	List[str]:







              """simple docstring"""
              if not self.model_tester.is_training:
                     return

              for model_class in self.all_model_classes:
                     UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_with_labels()
                     UpperCamelCase       			=		True

                     if model_class.__name__ in [
                         *get_values(lowerCAmelCase__      ),
                         *get_values(lowerCAmelCase__      ),
                     ]:
                            continue

                     UpperCamelCase       			=		model_class(lowerCAmelCase__      )
                     model.to(lowerCAmelCase__      )
                     model.train()
                     UpperCamelCase       			=		self._prepare_for_class(lowerCAmelCase__							,   lowerCAmelCase__							,   return_labels=lowerCAmelCase__      )
                     UpperCamelCase       			=		model(**lowerCAmelCase__      ).loss
                     loss.backward()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Tuple:







              """simple docstring"""
              if not self.model_tester.is_training:
                     return

              for model_class in self.all_model_classes:
                     UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_with_labels()
                     UpperCamelCase       			=		False
                     UpperCamelCase       			=		True

                     if (
                         model_class.__name__
                         in [*get_values(lowerCAmelCase__      ), *get_values(lowerCAmelCase__      )]
                         or not model_class.supports_gradient_checkpointing
                     ):
                            continue

                     UpperCamelCase       			=		model_class(lowerCAmelCase__      )
                     model.to(lowerCAmelCase__      )
                     model.gradient_checkpointing_enable()
                     model.train()
                     UpperCamelCase       			=		self._prepare_for_class(lowerCAmelCase__							,   lowerCAmelCase__							,   return_labels=lowerCAmelCase__      )
                     UpperCamelCase       			=		model(**lowerCAmelCase__      ).loss
                     loss.backward()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase       			=		model_class(lowerCAmelCase__      )
                     UpperCamelCase       			=		inspect.signature(model.forward      )
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     UpperCamelCase       			=		[*signature.parameters.keys()]

                     UpperCamelCase       			=		['''pixel_values''']
                     self.assertListEqual(arg_names[:1]							,   lowerCAmelCase__      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*lowerCAmelCase__      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	str:







              """simple docstring"""
              def check_hidden_states_output(UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Tuple      ):
                     UpperCamelCase       			=		model_class(lowerCAmelCase__      )
                     model.to(lowerCAmelCase__      )
                     model.eval()

                     with torch.no_grad():
                            UpperCamelCase       			=		model(**self._prepare_for_class(lowerCAmelCase__							,   lowerCAmelCase__      )      )

                     UpperCamelCase       			=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                     UpperCamelCase       			=		self.model_tester.num_stages
                     self.assertEqual(len(lowerCAmelCase__      )							,   expected_num_stages + 1      )

                     # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
                     self.assertListEqual(
                         list(hidden_states[0].shape[-2:]      )							,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,   )

              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase       			=		True
                     check_hidden_states_output(lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__      )

                     # check that output_hidden_states also work using config
                     del inputs_dict["output_hidden_states"]
                     UpperCamelCase       			=		True

                     check_hidden_states_output(lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__      )






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	List[Any]:







              """simple docstring"""
              for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase       			=		ConvNextVaModel.from_pretrained(lowerCAmelCase__      )
                     self.assertIsNotNone(lowerCAmelCase__      )





def 						lowerCamelCase__      (				)->				Tuple:







       """simple docstring"""
       UpperCamelCase       			=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
       return image



@require_torch
@require_vision
class 	__a	(   unittest.TestCase						):






       @cached_property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	int:







              """simple docstring"""
              return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"      ) if is_vision_available() else None






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"      ).to(lowerCAmelCase__      )

              UpperCamelCase       			=		self.default_image_processor
              UpperCamelCase       			=		prepare_img()
              UpperCamelCase       			=		preprocessor(images=lowerCAmelCase__							,   return_tensors="pt"      ).to(lowerCAmelCase__      )

              # forward pass
              with torch.no_grad():
                     UpperCamelCase       			=		model(**lowerCAmelCase__      )

              # verify the logits
              UpperCamelCase       			=		torch.Size((1, 1_000)      )
              self.assertEqual(outputs.logits.shape							,   lowerCAmelCase__      )

              UpperCamelCase       			=		torch.tensor([0.9996, 0.1966, -0.4386]      ).to(lowerCAmelCase__      )
              self.assertTrue(torch.allclose(outputs.logits[0, :3]							,   lowerCAmelCase__							,   atol=1e-4      )      )










'''simple docstring'''



def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return x if y == 0 else greatest_common_divisor(A       ,		x % y					)
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return (x * y) // greatest_common_divisor(A       ,		A					)
def        UpperCAmelCase (    A	:					int = 20					):
							SCREAMING_SNAKE_CASE       :   Tuple         =      1
							for i in range(1       ,		n + 1					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      lcm(A       ,		A					)
							return g


if __name__ == "__main__":
							print(f'{solution() = }')




